This work addresses the maneuvering target localization problem with measurements of bistatic range (BR), bistatic range rate (BRR) and derivative of bistatic range rate (DBRR) from a distributed multiple input multiple output (MIMO) radar. To achieve high robustness to outliers, we adopt an ℓ1 norm based approach, and formulate a least absolute deviation (LAD) problem with heterogeneous measurements for jointly estimating target motion parameters of position, velocity and acceleration. Then we construct a factor graph representation by converting LAD into reweighted least squares (RLS) problem, and propose an iterative message passing localization algorithm. While demon strated with BR, BRR and DBRR, the proposed factor graph frame work supports arbitrary heterogeneous measurement combinations. Cram´ er-Rao Lower Bound (CRLB) is derived for performance evaluation. Simulations show that the proposed method has superior localization performance and robustness in the presence of measurement outliers, and notably outperforms benchmark methods under moderate to high noise conditions. Results show that our proposed method delivers a performance improvement of over 25dB than compared methods, when the upper bound of BR outlier exceeds 50m.